Measurement of the charge asymmetry in top-quark pair production in association with a photon with the ATLAS experiment

Physics Letters B 843 (2023) 137848 Contents lists available at ScienceDirect Physics Letters B journal homepage: www.elsevier.com/locate/physletb Measurement of the charge asymmetry in top-quark pair production in association with a photon with the ATLAS experiment .The ATLAS Collaboration a r t i c l e i n f o a b s t r a c t Article history: Received 21 December 2022 Received in revised form 17 February 2023 Accepted 14 March 2023 Available online 20 June 2023 Editor: M. Doser A measurement of the charge asymmetry in top-quark pair (t¯ t) production in association with a photon is presented. The measurement is performed in the single-lepton t¯ tdecay channel using proton–proton collision data collected with the ATLAS detector at the Large Hadron Collider at CERN at a centre-of-massenergy of 13 TeV during the years 2015–2018, corresponding to an integrated luminosity of 139 fb−1. The charge asymmetry is obtained from the distribution of the difference of the absolute rapidities of the top quark and antiquark using a profile likelihood unfolding approach. It is measured to be AC= −0.003 ±0.029 in agreement with the Standard Model expectation. ©2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/). Funded by SCOAP3. 1. Introduction The top quark is the heaviest known elementary particle and the only quark that decays before hadronisation, which allows direct access to its properties in production and decay. Measurements of top-quark properties, predicted by the Standard Model (SM), provide important input to test theoretical calculations and have the potential to reveal deviations from the SM predictions. One of the relevant properties is related to the slight difference between the rapidity distributions of top quarks and top antiquarks produced in pairs (t¯ t). This asymmetry, referred to as charge asymmetry, is defined in proton–proton collisions as follows [1–4]: AC=N(|yt|>|y¯ t|)−N(|yt|<|y¯ t|) N(|yt|>|y¯ t|)+N(|yt|<|y¯ t|), where Nis the number of events and yt(y¯ t) the rapidity of the top quark (top antiquark). The production of t¯ tevents is predicted to be symmetric under the exchange of top quark and antiquark, i.e. AC=0, at leading-order (LO) accuracy in perturbative QCD. However, at next-to-leading order (NLO), quark–antiquark-initiated t¯ tproduction is asymmetric in the top-quark rapidity distribution, owing to interference between processes with initialand finalstate gluon emission and between the Born diagram and the box diagram at O(α4 s). The total asymmetry from the sum of all effects is expected to be positive [5]. Previous measurements of the asymmetry in t¯ tproduction by the ATLAS and CMS Collaborations at centre-of-mass energies (√s) of 7, 8 and 13 TeV [6–16]agree with the SM expectation. The most E-mail address: atlas -publications @cern .ch. recent measurement of the inclusive and differential charge asymmetry at 13 TeV by the ATLAS Collaboration [17]reported evidence for a non-zero asymmetry in t¯ tproduction (measured as AC= 0.0068 ±0.0015 and in agreement with the SM prediction [17,18]). While ACat the LHC corresponds to a central–forward asymmetry, the t¯ tproduction asymmetry manifests itself as a forward– backward asymmetry at the Tevatron. Early measurements of this asymmetry showed deviations from NLO QCD predictions, particularly at large values of the t¯ tinvariant mass [19]. However, more recent results by the CDF and D0 Collaborations [20–22]are compatible with the improved SM predictions including NLO electroweak (EW) and higher-order QCD corrections [23,24]. The t¯ tcharge asymmetry is diluted at the LHC owing to the large fraction of gluon–gluon-initiated t¯ tevents, which are symmetric under the exchange of the top quark and antiquark. However, it is enhanced in other topologies where the fraction of quark–antiquark-initiated production is larger, such as in associated production of t¯ twith a photon (t¯ tγ)[1,2]. Interference effects among QCD diagrams at NLO, similar to those in t¯ tevents, are predicted in t¯ tγproduction. However, the dominant contribution to the asymmetry in t¯ tγarises from interference between QED initial-state radiation, Fig. 1(left), and final-state radiation, Fig. 1(right), which yields a larger asymmetry of negative sign. In addition, other QCD–EW higher-order contributions can have a sizeable effect on the observed asymmetry [25]. The overall asymmetry in t¯ tγat √s=13 TeV is expected to have a negative value, of 1%–2% depending on the phase space, according to SM predictions [25,26]and can be modified by ‘beyond-the-SM’ contributions. Contributions from an s-channel colour octet or a Zboson would, for instance, result in a smaller ACabsolute value [1]. These sources of asymmetry are only present in the t¯ tγevents where the photon is radiated from an initial-state parton or one of https://doi.org/10.1016/j.physletb.2023.137848 0370-2693/©2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/). Funded by SCOAP3. The ATLAS Collaboration Physics Letters B 843 (2023) 137848 Fig. 1. Example Feynman diagrams of t¯ tγproduction contributing to the charge asymmetry. the top quarks (hereafter referred to as t¯ tγproduction). The asymmetry is diluted by t¯ tγevents where the photon arises from any of the charged decay products of the t¯ tsystem (t¯ tγdecay in the following). Therefore, only t¯ tγproduction events are considered as signal in this analysis. This paper presents the first measurement of the charge asymmetry of the top-quark pairs in t¯ tγproduction in t¯ tsingle-lepton final states, which have one high-pTlepton and at least four jets, two of which arise from b-quarks. It is performed using the full 139 fb−1data set recorded with the ATLAS detector between 2015 and 2018 at √s=13 TeV, referred to as Run 2. In order to extract the asymmetry, the top quarks are reconstructed using a kinematic likelihood fit. The separation between signal and background processes is enhanced using a neural network (NN) approach. The output distribution of the NN is used to define two regions, one enriched in background events and one in signal events. The AC value is determined by means of a maximum-likelihood fit to the distribution of the difference of absolute rapidities of the top quark and antiquark. This is also referred to as ‘maximum-likelihood unfolding’. The paper is organised as follows. The ATLAS detector is briefly introduced in Section 2. The simulation of signal and background processes is summarised in Section 3. The event reconstruction, selection, and estimation of the background processes are presented in Sections 4and 5. The systematic uncertainties are described in Section 6. The analysis strategy is discussed in Section 7, followed by the result in Section 8. Finally, a summary is given in Section 9. 2. ATLAS detector The ATLAS [27–29]detector is a multipurpose detector with a forward–backward symmetric cylindrical geometry with respect to the LHC beam axis.1The innermost layers consist of tracking detectors in the pseudorapidity range |η| <2.5. This inner detector (ID) is surrounded by a thin superconducting solenoid that provides a 2T axial magnetic field. It is enclosed by the electromagnetic and hadronic calorimeters, which cover |η| <4.9. The outermost layers of ATLAS consist of an external muon spectrometer within |η| <2.7, incorporating three large toroidal magnetic assemblies with eight coils each. The field integral of the toroids ranges between 2.0 and 6.0 Tm for most of the acceptance. The muon spectrometer includes precision tracking chambers and fast detectors for triggering. A two-level trigger system [30]reduces the recorded event rate to an average of 1kHz. An extensive software suite [31]is used in data simulation, in the reconstruction 1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, φbeing the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θas η=− lntan(θ/2), and the rapidity as y =(1/2)[(E+pz)/(E−pz)]. Angular distance is measured in units of R ≡(η)2+(φ)2. and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment. 3. Simulation of signal and background processes Monte Carlo (MC) event generators were used to estimate the contributions from the expected signal and background processes. The response of the ATLAS detector was simulated [32]with Geant4[33]. A fast simulation (AtlFast-II), which relies on a parameterisation of the calorimeter response [34], was used in samples employed to estimate uncertainties related to the t¯ tand tWγ modelling. The additional pp collisions in the same or neighbouring bunch crossings, referred to as pile-up, were generated with Pythia 8.186 [35]using a set of tuned parameters called the A3 tune [36] and the NNPDF2.3lo parton distribution function (PDF) set [37]. The signal t¯ tγproduction events were simulated with MadGraph5_aMC@NLO 2.7.3 [38]as a 2 →3 process at NLO accuracy in QCD. The interference effects between initial-state and finalstate photon radiation were considered in this process. The finalstate top quarks in the t¯ tγproduction sample are on-shell and were decayed at LO using MadSpin [39,40]to preserve spin correlations. The background t¯ tγdecay events, where the photon arises from any of the decay products of the top quarks or one of the onshell top quarks, were simulated with the same version of MadGraph5_aMC@NLO but at LO precision as a 2 →2 process followed by the decay of the top quarks, also simulated at LO precision. Both samples were generated using the NNPDF3.0nlo [41]PDF set and interfaced to Pythia 8.240 [42], which used the A14 tune [43] and the NNPDF2.3lo PDF set to model the parton shower, hadronisation, fragmentation and underlying event. The renormalisation and factorisation scales were set to 0.5 ×im2 i+p2 T,i, where mi and pT,iare the masses and transverse momenta of the particles generated from the matrix element (ME) calculation. Photons are required to have pT>15 GeV and to be isolated according to a smooth-cone hadronic isolation criterion with δ0=0.1, γ=0.1 and n =2, defined in Ref. [44], which avoids infrared divergences. The top-quark mass in the t¯ tγsample and all other samples involving top quarks was set to 172.5 GeV and the decays of bottom and charm hadrons were simulated using the EvtGen 1.6.0 program [45]. The t¯ tγproduction sample is normalised to the NLO cross section given by the MC simulation, while the normalisation of the t¯ tγdecay sample is corrected by a NLO/LO inclusive K-factor of 1.5. This K-factor was derived by comparing the normalisation of the sum of the NLO t¯ tγproduction sample and the LO t¯ tγdecay sample with the normalisation of a LO inclusive 2 →7t¯ tγsample corrected with the K-factor obtained in Ref. [46]using the calculation described in Ref. [47]. The t¯ tevents were simulated at NLO accuracy in QCD using Powheg Box v2[48–50]and the NNPDF3.0nlo PDF set. The parton shower was generated with Pythia 8.230 using the A14 tune [51]. The t¯ tevents are normalised to a cross-section value calculated with the Top++ 2.0 program at next-to-next-to-leading order (NNLO) in perturbative QCD, including soft-gluon resummation to next-to-next-to-leading-logarithm order (see Ref. [52] and references therein). The tWγevents were generated at LO accuracy with the MadGraph5_aMC@NLO 2.7.3 generator in the five-flavour scheme. To simulate this process, two complementary samples were generated; one as a 2 →3 process assuming a stable top quark and the other as a 2 →2 process, where the photon is radiated from any other charged final-state particle. To avoid infrared divergences, the photon was required to have pT>15 GeV and |η| <5.0 and to be 2 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 separated by R >0.2from any parton. Both samples make use of the NNPDF2.3lo PDF set and were interfaced to Pythia 8.212 for parton showering using the A14 tune. Single-top-quark sand t-channel production and inclusive tW production were simulated at ME level at NLO in QCD with Powheg Box v2 and the NNPDF2.3lo PDF set. The event generator was interfaced to Pythia 8.230, which used the A14 tune. The events are normalised to the NNLO cross section [53–55]. Events with Wγand Zγfinal states (with additional jets) were simulated with Sherpa 2.2.8 [56]at NLO in QCD using the NNPDF3.0nnlo PDF set. The samples are normalised to the cross sections given by the MC simulation. The Sherpa generator performs all steps of the event generation, from the hard process to the observable particles. Events with inclusive Wand Z-boson production in association with additional jets were simulated with Sherpa 2.2.1 [56,57]at NLO in QCD. The NNPDF3.0nlo PDF set was used together with a dedicated tune provided by the Sherpa authors. The samples are normalised to the NNLO cross section in QCD [58]. Diboson processes, WW, WZ and ZZ, were generated with Sherpa 2.2.2 (leptonic decays) and 2.2.1 (non-leptonic final states) at LO in QCD. The NNPDF3.0nnlo PDF set was used with a dedicated tune provided by the Sherpa authors. The samples are normalised to NLO cross sections in QCD [59]. Events with a t¯ tpair and an associated Wor Zboson (t¯ tV) were simulated at NLO at the ME level with MadGraph5_aMC@NLO using the NNPDF3.0nlo PDF set. The ME generator was interfaced to Pythia 8.210, for which the A14 tune was used in conjunction with the NNPDF2.3lo PDF set. The samples are normalised to NLO in QCD and electroweak theory [60]. A procedure was applied to remove the overlap between the samples in which events were generated at ME level without explicitly including a photon in the final state and the dedicated samples where photons were included in the ME-level eventgeneration step (t¯ tγand tWγfinal states, as well as Wγand Zγfinal states with additional jets). Events in the inclusive samples are discarded if they contain a parton-level photon that fulfils pT(γ) >15GeV and R(γ, ) >0.2, where pT(γ)is the transverse momentum of the photon and R(γ, ) is the angular distance between the photon and any charged lepton. Corrections to the pile-up profile, the trigger, reconstruction and selection efficiencies, and the energy scales and resolutions, are applied to the MC simulation samples to improve the description of the data. 4. Event reconstruction and selection The analysis uses a data set that passes stringent quality requirements and corresponds to an integrated luminosity of 139 fb−1collected with the ATLAS detector during Run 2 of the LHC. Events are required to have at least one primary vertex reconstructed from at least two associated tracks, and only events where at least one single-electron [61]or single-muon [62]trigger was fired are selected. Muons are reconstructed by combining a track in the muon spectrometer with a track in the ID system. The reconstruction, identification and calibration methods are described in Ref. [63]. The muon track is also required to originate from the primary collision vertex. Muons are required to be isolated according to measurements of nearby track pTand calorimeter energy. Only muons with calibrated pT>25 GeV and |η| <2.5 and passing ‘medium’ quality requirements are considered. Electrons are reconstructed from energy deposits in the electromagnetic calorimeter (ECAL) associated with reconstructed tracks in the ID system. The origin of the electron track also has to be compatible with the primary vertex. Electrons are identified with a combined likelihood technique [64]using a ‘tight’ working point, and are required to be isolated according to measurements of nearby calorimeter energy and track pT. Electrons are calibrated with the method described in Ref. [64] and are selected if they fulfil pT>25GeV and |ηclus| <2.47, excluding the ECAL barrel/endcap transition region 1.37 <|ηclus| <1.52, where ηclus refers to the pseudorapidity of the calorimeter energy cluster associated with the electron. Photons are reconstructed from energy deposits in the central region of the ECAL [64]. Photons are required to fulfil tight identification and isolation requirements. The latter is defined as Eiso T R<0.4<0.022 ·ET(γ) +2.45GeV in conjunction with piso T R<0.2<0.05 ·ET(γ), where Eiso Trefers to the calorimeter isolation within a cone of size R =0.4around the direction of the photon candidate and piso Tis the track isolation within a R =0.2 cone [65]. Photons are required to have transverse energy ET>20GeV and |ηclus| <2.37, excluding the calorimeter transition region. They are separated into two categories, one where the cluster is not matched to any reconstructed track in the ID system (unconverted photons) and the other where the cluster is matched to one or two reconstructed tracks that are consistent with originating from a photon conversion and, in addition, a conversion vertex can be found (converted photons). Jets are reconstructed using the anti-ktalgorithm [66]in the FastJet implementation [67]with a distance parameter R =0.4. Their reconstruction is performed on particle-flow objects [68]. The jet energy scale and jet energy resolution are calibrated using an energyand η-dependent calibration scheme based on simulation with in situ corrections obtained from data [69]. Only jets with pT>25GeV and |η| <2.5are considered in the analysis. Jets with a large contribution from pile-up vertices are identified with the jet vertex tagger (JVT) [70] and rejected. Jets arising from b-quark hadronisation, referred to as b-jets, are identified using the DL1r b-tagging algorithm [71], which is based on an artificial deep neural network combining information from other algorithms using track impact parameters and secondary vertices, and a multi-vertex reconstruction algorithm. The flavourtagging efficiency for b-jets, as well as for c-jets and light-flavour jets, is calibrated as described in Ref. [72]. The working point used to select the b-jets corresponds to a selection efficiency of 77% in simulated t¯ tevents. The magnitude of the reconstructed missing transverse momentum (Emiss T)[73,74]is calculated from the negative vector sum of the pTof all calibrated physics objects and the remaining unclustered energy, also called the soft term. This term is estimated from low-pTtracks associated with the primary vertex but not with any reconstructed object. An overlap removal procedure is implemented to avoid the reconstruction of the same energy clusters or tracks as different objects. Electron candidates that share their track with a muon candidate are removed and jets within a R =0.2 cone around any of the remaining electrons are excluded. If the distance between an electron and any remaining jet is R <0.4, the electron is subsequently removed. In the next step, muon candidates within R =0.4of a jet are removed if the jet has more than two associated tracks, otherwise the jet is discarded. In the final step, photons within a R =0.4 cone around any remaining electron or muon are excluded and then jets within a R =0.4 cone around any remaining photon are removed. Events are selected if they have exactly one electron or one muon that is matched to the corresponding trigger-level object. The pTthresholds for the leptons are 25 GeV in 2015 data, 27 GeV in 2016 data, and 28 GeV in 2017 and 2018 data, which are at least 1 GeV above the pTthresholds of the single-lepton triggers. This is done to avoid differences due to the calibration of the objects used in the trigger logic, and objects used in the physics analysis. Only 3 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 events containing exactly one reconstructed photon fulfilling the condition R(, γ) >0.4are considered in the measurement. Additionally, events where the invariant mass of the electron–photon system is within 5GeVof the Z-boson mass are rejected. The event is also required to have at least four jets and at least one of them must be b-tagged. The kinematic properties, in particular the rapidities of the top quark and antiquark, are determined by means of a constrained kinematic fitting algorithm, KLFitter [75], based on a maximumlikelihood approach applied to the four-momenta of the selected lepton and up to five leading jets (pT-ordered) and Emiss T, representing the transverse momentum of the neutrino. The likelihood is constructed as the product of transfer functions that relate the energies of the reconstructed objects and parton-level objects and Breit–Wigner distributions that match the selected objects to the Wbosons and the top quarks. The fit is constrained to reconstruct two Wbosons, each with a mass of 80.4 GeV. In addition, the reconstructed masses of the top quark and antiquark are constrained to 172.5 GeV. The combination of jets that gives the highest likelihood is selected and the jets are used to reconstruct the hadronically and leptonically decaying top quark or antiquark. In the case of leptonic top-quark decay, the invariant mass of the lepton–neutrino system is constrained to be the W-boson mass and a quadratic equation for the neutrino’s longitudinal momentum is obtained. For real solutions of the equation, the solution that results in the top-quark mass closer to 172.5 GeV is chosen, while in the case of complex solutions, the real part of the solution is considered. The fraction of top quarks that are reconstructed within R =1.0of the parton-level top quarks is about 64% for hadronically decaying top quarks and 72% for leptonically decaying top quarks. 5. Background estimation Each background process is assigned to one of several categories depending on the origin of the reconstructed photon, whether it is a prompt photon or whether another object mimics a photon signature. The estimation of background events from different sources closely follows the methods employed in Ref. [46]. After the event selection, the largest background contribution is that of t¯ tγdecay events (about 30% of the total number of events). The prompt γbackground category, which contains any other type of background process with a prompt photon, constitutes about 15% of the selected events. Both contributions are estimated using MC simulation. Agreement between data and simulation of the prompt γbackground was validated in dedicated regions. A validation region enriched in the Zγprocess is defined by selecting events with exactly one photon, two same-flavour opposite-sign leptons, fewer than four jets and no b-tagged jets. Events in the Wγvalidation region fulfil the same lepton and photon requirements as in the signal region. The simulation of Wγis known to underestimate the number of events with heavy-flavour jets in data. Thus, to define a region orthogonal to the signal region while selecting events with heavy-flavour content, the events in the validation region are additionally required to have fewer than four jets and at least one must pass the b-tagging working point with a selection efficiency of 85% but fail the one with 70% efficiency in simulated t¯ tevents. The expected fractions of Zγand Wγevents in the corresponding control regions are about 95% and 50%, respectively. Another significant contribution arises from processes with an electron mimicking a photon signature in the detector, referred to as e-fake. This background contribution amounts to 16% of the total number of selected events and it is estimated from data by applying a tag-and-probe method to Z→e+e−events [65]. Two control regions are defined in order to determine the e-fake photon rate in data and simulation: one region contains events with an electron–positron pair and the other, enriched in e-fake photon events, contains events with an electron and a photon satisfying the object selection criteria described in Section 4. Additionally, the invariant mass of the pair of objects is required to be in the range [40, 140] GeV, and their angular separation in φmust exceed 2.62 rad to suppress the contributions from events where the photon is radiated from the electron. Background contributions not originating from Z-boson events are subtracted in data with a fit of the invariant mass distribution. The Z→e+e−γcontribution with prompt photons where one of the electrons is not reconstructed or identified is subtracted using simulation. The e-fake photon rate is obtained as the ratio of the event yield after background subtraction in the e-fake-enriched control region to the yield in the electron–positron control region. The calculated ratio is binned in the pT(three bins) and |η|(four bins) of the photon in the eγ events and either the electron or the positron in the e+e−events, selected randomly to avoid biasing the selection, and separately for converted and unconverted photons. Scale factors are calculated to correct the e-fake background estimate in the signal region, based on a comparison between the e-fake photon rates obtained using either data or simulation. The systematic uncertainties in the scale factors account for possible mismodelling of the signal and background processes in the fit. The values of the scale factors vary from 0.8 to 1.4 with uncertainties between 5% and 20%. The background contribution from events where the photon signature arises from hadronic energy depositions in the ECAL or from hadron decays such as π0→γγ, generically referred to as h-fake, constitutes about 7% of the events. The h-fake background is estimated from data by using the so-called ABCD method. Three orthogonal regions enriched with h-fake photon events are defined by inverting the photon isolation selection and the requirements on four variables related to the shower shape in the first layer of the ECAL, which are part of the photon tight identification criteria. They are chosen because of their small correlation with the photon isolation and their power to discriminate between prompt and h-fake photons. Events are selected for regions A and B if their photon fails at least two out of four identification requirements, while satisfying all other identification criteria, and pass or fail the isolation requirements, respectively. Region C contains events where the photons fail the isolation requirements but satisfy the tight identification criteria. Additionally, the sum of the pT of all tracks within R =0.2of the photon is required to be larger than 3GeVto further suppress the prompt-photon contribution in regions B and C. The h-fake background contribution in the signal region is measured as the product of the numbers of events in regions A and C divided by the number of events in region B. The estimate is corrected for the correlation between the criteria, which is obtained using MC simulations. The scale factors are obtained separately for converted and unconverted photons and as a function of the photon pT(two bins) and |η|(four bins). The considered sources of systematic uncertainty in the h-fake background contribution include the modelling of the t¯ tprocess, which contributes about 90% of the h-fake events, the shower shapes, and the normalisation uncertainties of the background processes. The scale factors range from 0.6 to 1.5, with uncertainties of 30%–60%. The contribution from events with a non-prompt or misidentified lepton, referred to as lepton fake, is obtained using the datadriven approach referred to as the matrix method [76]. Events are separated into two categories that are based on tighter or looser lepton identification and isolation requirements, and thus enriched in events with real leptons or non-prompt/fake leptons, respectively. The contribution in the signal region is estimated from the data events passing loose lepton selection requirements, corrected by a weight that depends on the realand fake-lepton efficiencies obtained from the two event categories described above. The ef4 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 ficiencies are parameterised as a function of the lepton kinematic properties. The uncertainties are estimated by using different parameterisations and tighter control regions, and by including the normalisation uncertainty of the prompt-lepton background. This background contribution amounts to around 1% of all selected events, dominated by events with a misidentified electron. 6. Systematic uncertainties The precision of the obtained asymmetry ACis affected by several sources of systematic uncertainty, arising from detector effects or theoretical assumptions, as well as uncertainties due to the limited number of events in the MC simulations. The different sources of systematic uncertainty, discussed in the following, affect the event yields, the distribution shape of the observable of interest, or both. The sources of systematic uncertainty affecting the shape of the distribution typically have a larger impact on the precision of the result because global normalisation uncertainties cancel out in the ratio of event yields that defines AC. The experimental systematic effects include uncertainties in the integrated luminosity and the simulation of pile-up events, as well as effects related to the reconstruction and identification of the physics objects in the analysis. The uncertainty in the total integrated luminosity is estimated to be 1.7% [77], using the LUCID-2 detector [78]for the primary luminosity measurements. The uncertainty associated with the modelling of pile-up is determined by varying the pile-up reweighting in the simulation within its uncertainties. The photon and lepton identification and isolation efficiencies, momentum scale and resolution [79,80], and lepton trigger efficiencies in simulation are corrected using scale factors to better describe the corresponding values in data. These corrections, which typically depend on pTand η, are varied within their uncertainties to estimate the corresponding systematic uncertainty. The jet energy scale (JES) uncertainty is derived from a combination of simulations, test-beam data and in situ measurements [69]. Contributions from jet-flavour composition, ηintercalibration, punch-through, single-particle response, calorimeter response to different jet flavours, and pile-up are also taken into account, yielding a total of 30 uncorrelated JES uncertainty subcomponents, of which 29 are non-zero in a given event depending on the type of simulation used. The jet energy resolution in simulation is smeared by its corresponding uncertainty [81]split into eight uncorrelated sources. The uncertainty associated with the JVT discriminant for pile-up jet rejection is obtained by varying the efficiency correction factors. The uncertainties in the b-jet tagging calibration are determined separately for b-jets, c-jets and light-flavour jets [82–84]. For each jet category, the uncertainties are decomposed into several uncorrelated components. The uncertainty in Emiss Tarises from the propagation of the energy scales and resolutions of photons, leptons and jets, and the modelling of its soft term [74]. The signal and background modelling uncertainties include those owing to the choice of QCD scales, parton shower, amount of QCD initial-state radiation (ISR), and PDF set. The effect of the QCD scale uncertainty for each of the t¯ tγ, tWγand t¯ tprocesses is evaluated independently by separately halving and doubling the renormalisation and factorisation scales relative to the default scale choice. The uncertainty from the parton shower and hadronisation for those processes is estimated by comparing the nominal simulated samples interfaced with Pythia 8 with alternative samples interfaced to Herwig 7[85,86]. Uncertainties due to the value of αsused in the ISR parton shower modelling are estimated by comparing the nominal t¯ tγ, tWγand t¯ tsimulations with alternative samples simulated with higher or lower radiation parameter settings in the A14 tune, controlled by the var3c parameter implemented in Pythia 8. An additional ISR uncertainty is obtained for the t¯ tprocess by comparing the nominal sample with an additional one with the hdamp parameter, which controls the pTof the first additional emission, varied by a factor of two [87]. The uncertainty in the PDFs for the signal and background t¯ tγprocesses is estimated using the 30 PDF variations of the PDF4LHC15 prescription [88]. The PDF variations are propagated by using alternative generator weights and each of them is considered as a separate nuisance parameter in the fit. For the e-fake, h-fake, and lepton-fake background contributions, the total uncertainties associated with the corrections obtained using data are propagated to the final result. A normalisation uncertainty of 20% is assigned to the t¯ tγdecay process, based on the estimated uncertainty in the NLO K-factor [46], and a 50% normalisation uncertainty is assigned to Wγ, based on the differences between data and simulation observed in the dedicated control region, and to the minor background processes contributing to the prompt-photon category, i.e. single top quark, t¯ tV, diboson, and Zγ. 7. Signal discrimination A multivariate analysis using a neural network is performed to further separate the t¯ tγsignal from the background processes. The NN is fully connected and consists of three hidden layers. The first two layers consist of 96 nodes and are followed by a batch normalisation layer. The third layer has 16 nodes. The hidden layers use a parametric ReLU activation function, while the output node uses a sigmoid activation function. The training is performed with Keras [89]with the TensorFlow [90] backend with binary crossentropy as a loss function. The overall events are split into a training and validation set (85%) and a testing set (15%). The first set of events is used in a 5-fold cross-validation: Events are split randomly into 5 folds, the model is trained on 4 folds and one fold is used for validation of the NN configuration. This procedure is repeated 5 times. The event weights are applied to the events in the training, testing and validation samples. The NN uses a total of 21 variables related to the kinematic properties of individual objects, such as the photon pTand η, event shape variables (e.g. Emiss Tand the scalar sum of the pTof the jets in the event), the number of b-tagged jets, the pseudo-continuous binned b-tagging discriminant [72], the photon conversion type, and invariant masses and angular separations of different objects in the event (e.g. the invariant mass and Rof the lepton or the photon and the closest b-tagged jet). Example variables from among those with the most discriminating power are shown in Fig. 2. The MC simulation describes the data within the uncertainties. The largest contributions to the MC uncertainty band are the normalisation uncertainties associated with the backgrounds with prompt photons. The resulting NN discriminant output, ONN, shown in Fig. 3, is used to divide the events into a background-enriched region and a signal-enriched region, defined by ONN <0.6and ONN ≥0.6, respectively. This threshold was optimised to give the smallest expected uncertainty in AC. The observed and expected signal and background event yields in the two regions are summarised in Table 1. The slight underestimate of the data by the SM prediction, observed in Fig. 2, is reflected in Fig. 3at large values of ONN because it is expected to partially come from the normalisation of the t¯ tγproduction simulation, which is a free parameter in the profile likelihood unfolding described in the following. 8. Results The value of ACis extracted from the |yt| −|y¯ t|distribution in a fiducial region defined at particle level. The top quark and 5 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 Fig. 2. Distributions of photon pT(left), angular separation of the lepton and the closest b-tagged jet (middle), and transverse mass of the leptonically decaying Wboson (right) before the fit. The uncertainty band includes all experimental and modelling systematic uncertainties (cf. Section 6) added in quadrature. Overflow events are included in the last bin of each distribution. The lower part of the plot shows the ratio of the data to the prediction. Fig. 3. Distribution of the NN output discriminant before the fit. The uncertainty band includes all experimental and modelling systematic uncertainties (cf. Section 6) added in quadrature. The lower part of the plot shows the ratio of the data to the prediction. antiquark are defined at parton level in the MC simulation after final-state radiation but before decay. The fiducial region at particle level is defined by applying selection requirements similar to those at reconstruction level to the stable particles after the event generation and before the detector simulation. The fiducial phase space is defined by requiring exactly one photon, exactly one electron or muon, and at least four jets, of which at least one must be a b-jet, defined as follows. Photons are required to not originate from a hadron decay, to have ET>20GeV and |η| <2.37, and to be isolated such that the sum of transverse momenta of all charged particles surrounding the photon within R ≤0.2must be less than 5% of its own pT. Muons and elecTable 1 Event yields before the profile likelihood unfolding after the full selection in the two regions defined by the NN discriminant value. The quoted uncertainties correspond to all statistical and systematic uncertainties (cf. Section 6) added in quadrature. ONN <0.6ONN ≥0.6 t¯ tγprod (signal) 6660 ±350 6910 ±340 t¯ tγdecay 14100 ±3100 1900 ±560 h-fake γ3400 ±1400 790 ±360 e-fake γ6420 ±860 1480 ±260 Prompt γ6400 ±2000 1300 ±400 Lepton fake 410 ±110 57 ±35 Total 37400 ±4500 12400 ±1100 Data 38527 13763 trons must have pT>25 GeV and |η| <2.5, and must not originate from hadron decays. The momenta of nearby photons, within a R =0.1 cone, are added to the lepton before applying the selection. Jets are clustered with the anti-ktalgorithm with a radius parameter of R =0.4. All stable particles are considered in the clustering, except for the selected electrons, muons and photons, and the neutrinos originating from the top quarks. Jets are required to have pT>25 GeV and |η| <2.5. A particle-level jet is identified as a b-jet if a hadron with pT>5GeV containing a b-quark is matched to the jet through a ghost-matching method [91]. Jets within R =0.4of lepton or isolated photon candidates are removed. The ACvalue is obtained by means of a simultaneous maximumlikelihood unfolding of the |yt| −|y¯ t|distributions in the two regions defined by the NN output discriminant. The efficiency of selecting and reconstructing an event that is generated in the fiducial phase space is about 30% in the two regions, while the fraction of events that fulfil the selection at reconstruction level but fail the particle-level requirements is about 20%. The fraction of events that are reconstructed in the |yt| −|y¯ t|bin where they were generated is approximately 75%. The parameters of interest, which float freely in the fit, are the signal strength of the bin |yt| −|y¯ t| >0 and AC, which replaces the signal strength of the other bin, using the following expression: AC=(μ+T+−μ−T−)/(μ+T++μ−T−). The signal strength μ+(μ−)is defined as the ratio of the measured cross section to the expected value given by the SM simulation in 6 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 Fig. 4. The distributions of |yt| −|y¯ t|after the fit in the two regions defined by the NN output. Underflow and overflow events are included in corresponding bins of the distributions. The uncertainty band represents the total post-fit uncertainties. Correlations among uncertainties are taken into account as determined in the fit. The lower part of the plot shows the ratio of the data to the prediction. the bin |yt| −|y¯ t| >0(< 0)at generator level. The variable T+/− represents the number of t¯ tγproduction events at particle level in the corresponding bin. No regularisation is applied. The systematic uncertainties are taken into account via nuisance parameters in the likelihood function. They are symmetrised, taking half of the difference between the upward and downward variations as the uncertainty. If both variations have the same sign, the average of the difference between each variation and the nominal value is chosen as the two-sided uncertainty. If only one variation is available (e.g. the uncertainty from the parton shower or the PDF uncertainties) the difference from the nominal value is taken as both the upward and downward variations for the corresponding source. In addition, a pruning procedure is implemented in order to remove the smallest systematic uncertainties. In addition to the NLO/LO K-factor applied to correct the normalisation of the t¯ tγdecay process, the |yt| −|y¯ t|template is reweighted to account for the asymmetry in t¯ tproduction. The weight is obtained at parton level by considering the central value of the prediction for the inclusive t¯ tasymmetry, ACt¯ t= 0.0064+0.0005 −0.0006, calculated at NNLO accuracy in QCD with EW corrections at NLO [17,18]. The weight is then propagated to the distribution at reconstruction level. For consistency, the MC templates obtained with the NLO t¯ tsamples are also reweighted to match that asymmetry value. To probe the robustness of the method, and in particular to verify that the unfolding procedure does not bias the result towards the asymmetry in the signal MC simulation, the measurement was repeated with pseudodata. Several pseudodata sets were obtained by reweighting the |yt| −|y¯ t|distribution of the signal t¯ tγproduction sample to correspond to different AC values and by adding the background contributions. The unfolding procedure was repeated using the nominal simulation. The resulting values of the asymmetry are in agreement with the true AC asymmetry of each pseudodata set within the statistical precision and do not indicate any bias. The post-fit |yt| −|y¯ t|distributions in the two regions are shown in Fig. 4. The ACof the t¯ tγproduction process is expected to have a negative sign, while the asymmetry of the background contributions with a top-quark pair, i.e. t¯ tγdecay and t¯ t, is expected to be positive as discussed in Section 1. Good agreement is observed between the data and the prediction after the fit. As a reTable 2 Summary of the impact of the systematic uncertainties on ACgrouped into different categories. The quoted uncertainties are obtained by repeating the fit with certain sets of nuisance parameters fixed to their post-fit values, and calculating the squared uncertainties as the difference of the squares of the fullfit and repeated-fit uncertainties. The category Other experimental includes uncertainties associated with leptons, pile-up and luminosity. Total uncertainty 0.029 Statistical uncertainty 0.024 MC statistical uncertainties Background processes 0.008 t¯ tγproduction 0.004 Modelling uncertainties t¯ tγproduction modelling 0.003 Background modelling 0.002 Prompt background normalisation 0.002 Experimental uncertainties Jet 0.009 Fake-lepton background estimate 0.005 Emiss T0.005 Fake-photon background estimates 0.003 Photon 0.001 b-tagging 0.001 Other experimental 0.004 sult of the fit, a few nuisance parameters are slightly constrained: the uncertainties in the normalisation of the t¯ tγdecay and Wγ backgrounds are reduced by 30% and 15%, respectively. In all cases, the best-fit values of the nuisance parameters are well within one standard deviation of their initial values. The asymmetry is found to be AC=−0.003 ±0.029 =−0.003 ± 0.024(stat) ±0.017(syst), assuming the SM t¯ tcharge asymmetry of At¯ t C=0.0064 [18]. The systematic uncertainty is derived from its squared value, calculated as the difference of the squares of the total uncertainty and the statistical uncertainty, obtained from a fit without systematic uncertainties. The ACvalue is compatible with the value obtained from the MadGraph5_aMC@NLO MC sim7 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 ulation in the same phase space, AC=−0.014 ±0.001(scale). The precision of the result is limited by the statistical uncertainty. The impact of the different sources of systematic uncertainty, grouped in categories, is summarised in Table 2. The most relevant sources of systematic uncertainty are the MC statistical uncertainty of the prompt-photon background and the experimental systematic sources related to jets and Emiss T. The dependence of the measured t¯ tγACon At¯ t Cis estimated by repeating the measurement for different values of the t¯ tasymmetry in the range between 0 and 2 ×At¯ t C. The dependence is found to be linear and can be parameterised as AC=−0.57 ×At¯ t C+0.0005. 9. Conclusion This paper presents a measurement of the top-quark pair charge asymmetry in t¯ tγevents using 139 fb−1of pp collision data at a centre-of-mass energy of 13 TeV collected by the ATLAS experiment at the LHC. The selected events have exactly one photon, one lepton, and at least four jets, of which at least one is b-tagged. The inclusive charge asymmetry yields AC=−0.003 ±0.029 =−0.003 ±0.024(stat) ±0.017(syst), which is compatible with the Standard Model prediction within the uncertainties. The precision is limited by the statistical uncertainty. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgements We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST,ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014–2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. 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Young142, M. Yuan105, R. Yuan62b,l, L. Yue95, X. Yue63a, M. Zaazoua35e, B. Zabinski85, E. Zaid52, T. Zakareishvili148b, N. Zakharchuk34, S. Zambito56, J.A. Zamora Saa136d,136b, J. Zang152, D. Zanzi54, O. Zaplatilek131, S.V. Zeißner49, C. Zeitnitz170, J.C. Zeng161, D.T. Zenger Jr26, O. Zenin37, T. Ženiš 28a, S. Zenz 93, S. Zerradi35a, D. Zerwas66, B. Zhang 14c, D.F. Zhang138, G. Zhang 14b, J. Zhang 62b, J. Zhang6, K. Zhang 14a,14d, L. Zhang 14c, P. Zhang 14a,14d, R. Zhang 169, S. Zhang105, T. Zhang 152, X. Zhang62c, X. Zhang62b, Y. Zhang 62c,5, Z. Zhang 17a, Z. Zhang 66, H. Zhao137, P. Zhao 51, T. Zhao 62b, Y. Zhao 135, Z. Zhao62a, A. Zhemchugov 38, X. Zheng62a, Z. Zheng142, D. Zhong161, B. Zhou105, C. Zhou169, H. Zhou7, N. Zhou62c, Y. Zhou 7, C.G. Zhu62b, C. Zhu14a,14d, H.L. Zhu62a, H. Zhu14a, J. Zhu105, Y. Zhu 62c, Y. Zhu 62a, X. Zhuang 14a, K. Zhukov37, V. Zhulanov37, N.I. Zimine38, J. Zinsser63b, M. Ziolkowski140, L. Živkovi´ c15, A. Zoccoli 23b,23a, K. Zoch56, T.G. Zorbas 138, O. Zormpa46, W. Zou 41, L. Zwalinski36 1Department of Physics, University of Adelaide, Adelaide; Australia 2Department of Physics, University of Alberta, Edmonton AB; Canada 3(a)Department of Physics, Ankara University, Ankara; (b)Division of Physics, TOBB University of Economics and Technology, Ankara; Türkiye 4LAPP, Univ. Savoie Mont Blanc, CNRS/IN2P3, Annecy; France 18 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 5APC, Université Paris Cité, CNRS/IN2P3, Paris; France 6High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America 7Department of Physics, University of Arizona, Tucson AZ; United States of America 8Department of Physics, University of Texas at Arlington, Arlington TX; United States of America 9Physics Department, National and Kapodistrian University of Athens, Athens; Greece 10 Physics Department, National Technical University of Athens, Zografou; Greece 11 Department of Physics, University of Texas at Austin, Austin TX; United States of America 12 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan 13 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 14 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing; (b)Physics Department, Tsinghua University, Beijing; (c)Department of Physics, Nanjing University, Nanjing; (d)University of Chinese Academy of Science (UCAS), Beijing; China 15 Institute of Physics, University of Belgrade, Belgrade; Serbia 16 Department for Physics and Technology, University of Bergen, Bergen; Norway 17 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley CA; (b)University of California, Berkeley CA; United States of America 18 Institut für Physik, Humboldt Universität zu Berlin, Berlin; Germany 19 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern; Switzerland 20 School of Physics and Astronomy, University of Birmingham, Birmingham; United Kingdom 21 (a)Department of Physics, Bogazici University, Istanbul; (b)Department of Physics Engineering, Gaziantep University, Gaziantep; (c)Department of Physics, Istanbul University, Istanbul; (d)Istinye University, Sariyer, Istanbul; Türkiye 22 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá; (b)Departamento de Física, Universidad Nacional de Colombia, Bogotá; Colombia 23 (a)Dipartimento di Fisica e Astronomia A. Righi, Università di Bologna, Bologna; (b)INFN Sezione di Bologna; Italy 24 Physikalisches Institut, Universität Bonn, Bonn; Germany 25 Department of Physics, Boston University, Boston MA; United States of America 26 Department of Physics, Brandeis University, Waltham MA; United States of America 27 (a)Transilvania University of Brasov, Brasov; (b)Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest; (c)Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi; (d)National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj-Napoca; (e)University Politehnica Bucharest, Bucharest; (f)West University in Timisoara, Timisoara; (g)Faculty of Physics, University of Bucharest, Bucharest; Romania 28 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava; (b)Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice; Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton NY; United States of America 30 Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, y CONICET, Instituto de Física de Buenos Aires (IFIBA), Buenos Aires; Argentina 31 California State University, CA; United States of America 32 Cavendish Laboratory, University of Cambridge, Cambridge; United Kingdom 33 (a)Department of Physics, University of Cape Town, Cape Town; (b)iThemba Labs, Western Cape; (c)Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg; (d)National Institute of Physics, University of the Philippines Diliman (Philippines); (e)University of South Africa, Department of Physics, Pretoria; (f)University of Zululand, KwaDlangezwa; (g)School of Physics, University of the Witwatersrand, Johannesburg; South Africa 34 Department of Physics, Carleton University, Ottawa ON; Canada 35 (a)Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies - Université Hassan II, Casablanca; (b)Faculté des Sciences, Université Ibn-Tofail, Kénitra; (c)Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech; (d)LPMR, Faculté des Sciences, Université Mohamed Premier, Oujda; (e)Faculté des sciences, Université Mohammed V, Rabat; (f)Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; Morocco 36 CERN, Geneva; Switzerland 37 Affiliated with an institute covered by a cooperation agreement with CERN 38 Affiliated with an international laboratory covered by a cooperation agreement with CERN 39 Enrico Fermi Institute, University of Chicago, Chicago IL; United States of America 40 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand; France 41 Nevis Laboratory, Columbia University, Irvington NY; United States of America 42 Niels Bohr Institute, University of Copenhagen, Copenhagen; Denmark 43 (a)Dipartimento di Fisica, Università della Calabria, Rende; (b)INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; Italy 44 Physics Department, Southern Methodist University, Dallas TX; United States of America 45 Physics Department, University of Texas at Dallas, Richardson TX; United States of America 46 National Centre for Scientific Research “Demokritos”, Agia Paraskevi; Greece 47 (a)Department of Physics, Stockholm University; (b)Oskar Klein Centre, Stockholm; Sweden 48 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen; Germany 49 Fakultät Physik, Technische Universität Dortmund, Dortmund; Germany 50 Institut für Kernund Teilchenphysik, Technische Universität Dresden, Dresden; Germany 51 Department of Physics, Duke University, Durham NC; United States of America 52 SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh; United Kingdom 53 INFN e Laboratori Nazionali di Frascati, Frascati; Italy 54 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg; Germany 55 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen; Germany 56 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland 57 (a)Dipartimento di Fisica, Università di Genova, Genova; (b)INFN Sezione di Genova; Italy 58 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen; Germany 59 SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow; United Kingdom 60 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble; France 61 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA; United States of America 62 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei; (b)Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao; (c)School of Physics and Astronomy, Shanghai Jiao Tong University, Key Laboratory for Particle Astrophysics and Cosmology (MOE), SKLPPC, Shanghai; (d)Tsung-Dao Lee Institute, Shanghai; China 63 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg; (b)Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg; Germany 64 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; (b)Department of Physics, University of Hong Kong, Hong Kong; (c)Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; China 65 Department of Physics, National Tsing Hua University, Hsinchu; Taiwan 66 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405, Orsay; France 67 Department of Physics, Indiana University, Bloomington IN; United States of America 68 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine; (b)ICTP, Trieste; (c)Dipartimento Politecnico di Ingegneria e Architettura, Università di Udine, Udine; Italy 69 (a)INFN Sezione di Lecce; (b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce; Italy 70 (a)INFN Sezione di Milano; (b)Dipartimento di Fisica, Università di Milano, Milano; Italy 71 (a)INFN Sezione di Napoli; (b)Dipartimento di Fisica, Università di Napoli, Napoli; Italy 72 (a)INFN Sezione di Pavia; (b)Dipartimento di Fisica, Università di Pavia, Pavia; Italy 19 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 73 (a)INFN Sezione di Pisa; (b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa; Italy 74 (a)INFN Sezione di Roma; (b)Dipartimento di Fisica, Sapienza Università di Roma, Roma; Italy 75 (a)INFN Sezione di Roma Tor Vergata; (b)Dipartimento di Fisica, Università di Roma Tor Vergata, Roma; Italy 76 (a)INFN Sezione di Roma Tre; (b)Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy 77 (a)INFN-TIFPA; (b)Università degli Studi di Trento, Trento; Italy 78 Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck; Austria 79 University of Iowa, Iowa City IA; United States of America 80 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 81 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora; (b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; (c)Instituto de Física, Universidade de São Paulo, São Paulo; (d)Rio de Janeiro State University, Rio de Janeiro; Brazil 82 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan 83 Graduate School of Science, Kobe University, Kobe; Japan 84 (a)AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow; (b)Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland 85 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 86 Faculty of Science, Kyoto University, Kyoto; Japan 87 Kyoto University of Education, Kyoto; Japan 88 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan 89 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 90 Physics Department, Lancaster University, Lancaster; United Kingdom 91 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom 92 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana; Slovenia 93 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom 94 Department of Physics, Royal Holloway University of London, Egham; United Kingdom 95 Department of Physics and Astronomy, University College London, London; United Kingdom 96 Louisiana Tech University, Ruston LA; United States of America 97 Fysiska institutionen, Lunds universitet, Lund; Sweden 98 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain 99 Institut für Physik, Universität Mainz, Mainz; Germany 100 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 101 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France 102 Department of Physics, University of Massachusetts, Amherst MA; United States of America 103 Department of Physics, McGill University, Montreal QC; Canada 104 School of Physics, University of Melbourne, Victoria; Australia 105 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 106 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 107 Group of Particle Physics, University of Montreal, Montreal QC; Canada 108 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany 109 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany 110 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 111 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 112 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen; Netherlands 113 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 114 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 115 (a)New York University Abu Dhabi, Abu Dhabi; (b)University of Sharjah, Sharjah; United Arab Emirates 116 Department of Physics, New York University, New York NY; United States of America 117 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan 118 Ohio State University, Columbus OH; United States of America 119 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 120 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 121 Palacký University, Joint Laboratory of Optics, Olomouc; Czech Republic 122 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America 123 Graduate School of Science, Osaka University, Osaka; Japan 124 Department of Physics, University of Oslo, Oslo; Norway 125 Department of Physics, Oxford University, Oxford; United Kingdom 126 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris; France 127 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 128 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 129 (a)Laboratório de Instrumentac¸ão e Física Experimental de Partículas -LIP, Lisboa; (b)Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa; (c)Departamento de Física, Universidade de Coimbra, Coimbra; (d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa; (e)Departamento de Física, Universidade do Minho, Braga; (f)Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada (Spain); (g)Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa; Portugal 130 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic 131 Czech Technical University in Prague, Prague; Czech Republic 132 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 133 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 134 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France 135 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 136 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago; (b)Millennium Institute for Subatomic physics at high energy frontier (SAPHIR), Santiago; (c)Instituto de Investigación Multidisciplinario en Ciencia y Tecnología, y Departamento de Física, Universidad de La Serena; (d)Universidad Andres Bello, Department of Physics, Santiago; (e)Instituto de Alta Investigación, Universidad de Tarapacá, Arica; (f)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso; Chile 137 Department of Physics, University of Washington, Seattle WA; United States of America 138 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 139 Department of Physics, Shinshu University, Nagano; Japan 140 Department Physik, Universität Siegen, Siegen; Germany 141 Department of Physics, Simon Fraser University, Burnaby BC; Canada 142 SLAC National Accelerator Laboratory, Stanford CA; United States of America 143 Department of Physics, Royal Institute of Technology, Stockholm; Sweden 144 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 145 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 146 School of Physics, University of Sydney, Sydney; Australia 20 The ATLAS Collaboration Physics Letters B 843 (2023) 137848 147 Institute of Physics, Academia Sinica, Taipei; Taiwan 148 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; (b)High Energy Physics Institute, Tbilisi State University, Tbilisi; (c)University of Georgia, Tbilisi; Georgia 149 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel 150 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel 151 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece 152 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan 153 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan 154 Department of Physics, University of Toronto, Toronto ON; Canada 155 (a)TRIUMF, Vancouver BC; (b)Department of Physics and Astronomy, York University, Toronto ON; Canada 156 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 157 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 158 United Arab Emirates University, Al Ain; United Arab Emirates 159 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 160 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 161 Department of Physics, University of Illinois, Urbana IL; United States of America 162 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia -CSIC, Valencia; Spain 163 Department of Physics, University of British Columbia, Vancouver BC; Canada 164 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 165 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 166 Department of Physics, University of Warwick, Coventry; United Kingdom 167 Waseda University, Tokyo; Japan 168 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel 169 Department of Physics, University of Wisconsin, Madison WI; United States of America 170 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 171 Department of Physics, Yale University, New Haven CT; United States of America aAlso Affiliated with an institute covered by a cooperation agreement with CERN. bAlso at An-Najah National University, Nablus; Palestine. cAlso at Borough of Manhattan Community College, City University of New York, New York NY; United States of America. dAlso at Bruno Kessler Foundation, Trento; Italy. eAlso at Center for High Energy Physics, Peking University; China. fAlso at Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki; Greece. gAlso at Centro Studi e Ricerche Enrico Fermi; Italy. hAlso at CERN, Geneva; Switzerland. iAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland. jAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain. kAlso at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece. lAlso at Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America. mAlso at Department of Physics and Astronomy, University of Louisville, Louisville, KY; United States of America. nAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel. oAlso at Department of Physics, California State University, East Bay; United States of America. pAlso at Department of Physics, California State University, Sacramento; United States of America. qAlso at Department of Physics, King’s College London, London; United Kingdom. rAlso at Department of Physics, Stanford University, Stanford CA; United States of America. sAlso at Department of Physics, University of Fribourg, Fribourg; Switzerland. tAlso at Department of Physics, University of Thessaly; Greece. uAlso at Department of Physics, Westmont College, Santa Barbara; United States of America. vAlso at Hellenic Open University, Patras; Greece. wAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain. xAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany. yAlso at Institute of Particle Physics (IPP); Canada. zAlso at Institute of Physics and Technology, Ulaanbaatar; Mongolia. aa Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan. ab Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgia. ac Also at L2IT, Université de Toulouse, CNRS/IN2P3, UPS, Toulouse; France. ad Also at Lawrence Livermore National Laboratory, Livermore; United States of America. ae Also at National Institute of Physics, University of the Philippines Diliman (Philippines); Philippines. af Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen; Germany. ag Also at Technical University of Munich, Munich; Germany. ah Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; China. ai Also at TRIUMF, Vancouver BC; Canada. aj Also at Università di Napoli Parthenope, Napoli; Italy. ak Also at University of Chinese Academy of Sciences (UCAS), Beijing; China. al Also at University of Colorado Boulder, Department of Physics, Colorado; United States of America. am Also at Washington College, Maryland; United States of America. an Also at Yeditepe University, Physics Department, Istanbul; Türkiye. ∗Deceased. 21